Spinal cord injury and disorders (SCI/D) are a substantial health concern impacting about 1.2 million Americans, and 45,000 veterans. The total economic burden of SCI/D is estimated at $9 billion/year to $400 billion in lifetime medical and loss-of-productivity costs. The most common clinical presentation is high cervical SCI/D which produces a broad spectrum of issues, including loss of hand function and autonomy, sensory changes, spasticity, pain and autonomic dysfunction, profoundly impacting quality of life. Restoring these broad functions is the goal of regenerative and rehabilitative therapeutic approaches for SCI/D. The VA Gordon Mansfield Spinal Cord Injury Consortium (VA-GMSCIC) is a VA-funded effort to develop late-stage translational therapeutics in a nonhuman primate (NHP) model in preparation for testing emerging therapeutic approaches clinically. Prior and current funding has focused on multifaceted data collection on each subject, with 5 different centers collaboratively collecting data, each within their specific domain of expertise (physiology, behavior, histology, neurorehabilitation, and molecular biology). This is an ideal use of the NHP model, as maximal information is collected on the performance of therapeutic approaches in a small number of NHPs. Yet the data from VA-GMSCIC is high-volume, high-complexity, and high-heterogeneity, providing both a challenge and opportunity for novel discoveries. Application of modern data science tools can help deliver on the promise of translational precision medicine for SCI/D. However, the VA-GMSCIC does not have dedicated funding to support modern big-data infrastructure for data integration, analysis and visualization. The proposed project will address this gap to deliver much-needed data science tools through an NHP Data Commons in collaboration with the Neuroscience Information Framework (NIF) to create open-source software for the data commons, an NHP ontology built on top of the NIF-Standard Ontology (NIFSTD), and a data analysis and visualization services. NIF is an NIH-Blueprint funded initiative that maintains the largest federated repository of neuroscience data, biomedical resources and neuroscience ontologies on the web. Our data-science team is uniquely-positioned to develop the proposed big-data-to-knowledge pipeline for SCI/D. Over the past 5 years we built the first multicenter, multispecies data repository for SCI/D, known as VISION-SCI (housing data on N>3500 SCI/D animals tracked on >20,000 variables). In the process of building VISION-SCI we developed tools and workflows for large-scale data curation, federated database systems, and cutting edge machine learning analytics for SCI/D discovery in mice, rats, monkeys and de-identified human clinical data. This prior work demonstrates proof-of-concept that a translational SCI/D data commons can deliver new discoveries about the nature of plasticity and recovery, as well as cross-species translation. For the proposed Aims we will apply these lessons to integrate VA-GMSCIC data by: establishing a secure NHP federated data commons (Aim 1), and then populating this data commons with: robotic rehabilitation data (Aim 2), open-field behavioral data (Aim 3), and imaging data including in vivo MRI data and postmortem microscopic studies (Aim 4). All of these data elements are currently collected on each NHP, however the data are stored in fragmented data systems ranging from siloed databases maintained by the individual collaborators to individual flat files and images. We will establish a dedicated data processing pipeline that will integrate these data for rapid decision- support in ongoing therapeutic development trials; novel data-driven-discovery; machine learning-based hypothesis generation; and confirmatory hypothesis testing. The NHP Data Commons will leverage existing data to support the past, present and future VA-GMSCIC rehabilitation and regeneration therapeutic studies and extend them with cutting-edge analytics. This represents a modern big-data approach to translation and will ensure that the existing VA investment in data collection is leveraged to the maximal extent through digital technologies for enduring knowledge-discovery from this valuable NHP model of SCI/D.